Improved I-vector-based Speaker Recognition for Utterances with Speaker Generated Non-speech sounds

Conversational speech not only contains several variants of neutral speech but is also prominently interlaced with several speaker generated non-speech sounds such as laughter and breath. A robust speaker recognition system should be capable of recognizing a speaker irrespective of these variations in his speech. An understanding of whether the speaker-specific information represented by these variations is similar or not helps build a good speaker recognition system. In this paper, speaker variations captured by neutral speech of a speaker is analyzed by considering speech-laugh (a variant of neutral speech) and laughter (non-speech) sounds of the speaker. We study an ivector-based speaker recognition system trained only on neutral speech and evaluate its performance on speech-laugh and laughter. Further, we analyze the effect of including laughter sounds during training of an i-vector-based speaker recognition system. Our experimental results show that the inclusion of laughter sounds during training seem to provide complementary speakerspecific information which results in an overall improved performance of the speaker recognition system, especially on the utterances with speech-laugh segments.